Ai sports betting algorithms engine

ABSTRACT

A system for wagering on outcomes of a live sporting event. The system further includes using AI to balance itself between how much it will lose bets to encourage game players to bet more, ultimately to win more profit and gain a larger game player user base. This allows the system to reduce immediate profits in exchange for long term profits due to a larger market share.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present patent application claims benefit and priority to U.S.patent application Ser. No. 17/108,343 entitled “AI SPORTS BETTINGALGORITHMS ENGINE” filed on Dec. 1, 2020 and U.S. Provisional PatentApplication No. 63/111,792 entitled “AI SPORTS BETTING ALGORITHMSENGINE” filed on Nov. 10, 2020, which is hereby incorporated byreference into the present disclosure.

FIELD

The present embodiments are generally related to play by play wageringon live sporting events

BACKGROUND

Offerors of fixed odd wagers set the odds they offer to a value thatwill statistically yield a profit despite the outcome of the event beingwagered on. However, because of the slight advantage of the wagerofferor, over time long-term players will begin to notice a pattern ofloss. Further, if odds are set too low, short term profits may increasebut less bets may be placed over time if there is a diminished incentiveto bet.

To remedy this issue, offerors of wagers often adjust odds to payoutbetter in order to incentivize current or past players or to draw in newplayers. However, this ultimately will result in some amount of loss tothe offeror of the wagers. Any entity that offers wagers must thenbalance the odds such that the entity is still profitable but alsoretains as many current players and draws in as many new players aspossible.

BRIEF DESCRIPTIONS OF THE DRAWINGS

The accompanying drawings illustrate various embodiments of systems,methods, and various other aspects of the embodiments. Any person withordinary skills in the art will appreciate that the illustrated elementboundaries (e.g. boxes, groups of boxes, or other shapes) in the figuresrepresent an example of the boundaries. It may be understood that, insome examples, one element may be designed as multiple elements or thatmultiple elements may be designed as one element. In some examples, anelement shown as an internal component of one element may be implementedas an external component in another, and vice versa. Furthermore,elements may not be drawn to scale. Non-limiting and non-exhaustivedescriptions are described with reference to the following drawings. Thecomponents in the figures are not necessarily to scale, emphasis insteadbeing placed upon illustrating principles.

FIG. 1: Illustrates a method of displaying a notification from a bettingapplication using AI that can impact normal betting, according to anembodiment.

FIG. 2: Illustrates a betting algorithms module, according to anembodiment.

FIG. 3: Illustrates a cross module, according to an embodiment.

FIG. 4: Illustrates a cross database, according to an embodiment.

FIG. 5: Illustrates a final odds module, according to an embodiment.

FIG. 6: Illustrates an AI comparison module, according to an embodiment.

FIG. 7: Illustrates a machine learning module, according to anembodiment.

FIG. 8: Illustrates an odds adjustment module, according to anembodiment.

FIG. 9: Illustrates an odds adjustment database, according to anembodiment.

DETAILED DESCRIPTION

Aspects of the present invention are disclosed in the followingdescription and related figures directed to specific embodiments of theinvention. Those of ordinary skill in the art will recognize thatalternate embodiments may be devised without departing from the spiritor the scope of the claims. Additionally, well-known elements ofexemplary embodiments of the invention will not be described in detailor will be omitted so as not to obscure the relevant details of theinvention.

As used herein, the word exemplary means serving as an example, instanceor illustration. The embodiments described herein are not limiting, butrather are exemplary only. It should be understood that the describedembodiments are not necessarily to be construed as preferred oradvantageous over other embodiments. Moreover, the terms embodiments ofthe invention, embodiments or invention do not require that allembodiments of the invention include the discussed feature, advantage,or mode of operation.

Further, many of the embodiments described herein are described in termsof sequences of actions to be performed by, for example, elements of acomputing device. It should be recognized by those skilled in the artthat the various sequence of actions described herein can be performedby specific circuits (e.g., application specific integrated circuits(ASICs)) and/or by program instructions executed by at least oneprocessor. Additionally, the sequence of actions described herein can beembodied entirely within any form of computer-readable storage mediumsuch that execution of the sequence of actions enables the processor toperform the functionality described herein. Thus, the various aspects ofthe present invention may be embodied in a number of different forms,all of which have been contemplated to be within the scope of theclaimed subject matter. In addition, for each of the embodimentsdescribed herein, the corresponding form of any such embodiments may bedescribed herein as, for example, a computer configured to perform thedescribed action.

With respect to the embodiments, a summary of terminology used herein isprovided.

An action refers to a specific play or specific movement in a sportingevent. For example, an action may determine which players were involvedduring a sporting event. In some embodiments, an action may be a throw,shot, pass, swing, kick, hit, performed by a participant in a sportingevent. In some embodiments, an action may be a strategic decision madeby a participant in the sporting event such as a player, coach,management, etc. In some embodiments, an action may be a penalty, foul,or type of infraction occurring in a sporting event. In someembodiments, an action may include the participants of the sportingevent. In some embodiments, an action may include beginning events ofsporting event, for example opening tips, coin flips, opening pitch,national anthem singers, etc. In some embodiments, a sporting event maybe football, hockey, basketball, baseball, golf, tennis, soccer,cricket, rugby, MMA, boxing, swimming, skiing, snowboarding, horseracing, car racing, boat racing, cycling, wrestling, Olympic sport,eSports, etc. Actions can be integrated into the embodiments in avariety of manners.

A “bet” or “wager” is to risk something, usually a sum of money, againstsomeone else's or an entity on the basis of the outcome of a futureevent, such as the results of a game or event. It may be understood thatnon-monetary items may be the subject of a “bet” or “wager” as well,such as points or anything else that can be quantified for a “bet” or“wager”. A bettor refers to a person who bets or wagers. A bettor mayalso be referred to as a user, client, or participant throughout thepresent invention. A “bet” or “wager” could be made for obtaining orrisking a coupon or some enhancements to the sporting event, such asbetter seats, VIP treatment, etc. A “bet” or “wager” can be done forcertain amount or for a future time. A “bet” or “wager” can be done forbeing able to answer a question correctly. A “bet” or “wager” can bedone within a certain period of time. A “bet” or “wager” can beintegrated into the embodiments in a variety of manners.

A “book” or “sportsbook” refers to a physical establishment that acceptsbets on the outcome of sporting events. A “book” or “sportsbook” systemenables a human working with a computer to interact, according to set ofboth implicit and explicit rules, in an electronically powered domainfor the purpose of placing bets on the outcome of sporting event. Anadded game refers to an event not part of the typical menu of wageringofferings, often posted as an accommodation to patrons. A “book” or“sportsbook” can be integrated into the embodiments in a variety ofmanners.

To “buy points” means a player pays an additional price (more money) toreceive a half-point or more in the player's favor on a point spreadgame. Buying points means you can move a point spread, for example up totwo points in your favor. “Buy points” can be integrated into theembodiments in a variety of manners.

The “price” refers to the odds or point spread of an event. To “take theprice” means betting the underdog and receiving its advantage in thepoint spread. “Price” can be integrated into the embodiments in avariety of manners.

“No action” means a wager in which no money is lost or won, and theoriginal bet amount is refunded. “No action” can be integrated into theembodiments in a variety of manners.

The “sides” are the two teams or individuals participating in an event:the underdog and the favorite. The term “favorite” refers to the teamconsidered most likely to win an event or game. The “chalk” refers to afavorite, usually a heavy favorite. Bettors who like to bet bigfavorites are referred to “chalk eaters” (often a derogatory term). Anevent or game in which the sports book has reduced its betting limits,usually because of weather or the uncertain status of injured players isreferred to as a “circled game.” “Laying the points or price” meansbetting the favorite by giving up points. The term “dog” or “underdog”refers to the team perceived to be most likely to lose an event or game.A “longshot” also refers to a team perceived to be unlikely to win anevent or game. “Sides”, “favorite”, “chalk”, “circled game”, “laying thepoints price”, “dog” and “underdog” can be integrated into theembodiments in a variety of manners.

The “money line” refers to the odds expressed in terms of money. Withmoney odds, whenever there is a minus (−) the player “lays” or is“laying” that amount to win (for example $100); where there is a plus(+) the player wins that amount for every $100 wagered. A “straight bet”refers to an individual wager on a game or event that will be determinedby a point spread or money line. The term “straight-up” means winningthe game without any regard to the “point spread”; a “money-line” bet.“Money line”, “straight bet”, “straight-up” can be integrated into theembodiments in a variety of manners.

The “line” refers to the current odds or point spread on a particularevent or game. The “point spread” refers to the margin of points inwhich the favored team must win an event by to “cover the spread.” To“cover” means winning by more than the “point spread”. A handicap of the“point spread” value is given to the favorite team so bettors can choosesides at equal odds. “Cover the spread” means that a favorite win anevent with the handicap considered or the underdog wins with additionalpoints. To “push” refers to when the event or game ends with no winneror loser for wagering purposes, a tie for wagering purposes. A “tie” isa wager in which no money is lost or won because the teams' scores wereequal to the number of points in the given “point spread”. The “openingline” means the earliest line posted for a particular sporting event orgame. The term “pick” or “pick 'em” refers to a game when neither teamis favored in an event or game. “Line”, “cover the spread”, “cover”,“tie”, “pick” and “pick-em” can be integrated into the embodiments in avariety of manners.

To “middle” means to win both sides of a game; wagering on the“underdog” at one point spread and the favorite at a different pointspread and winning both sides. For example, if the player bets theunderdog +4½ and the favorite −3½ and the favorite wins by 4, the playerhas middled the book and won both bets. “Middle” can be integrated intothe embodiments in a variety of manners.

Digital gaming refers to any type of electronic environment that can becontrolled or manipulated by a human user for entertainment purposes. Asystem that enables a human and a computer to interact according to setof both implicit and explicit rules, in an electronically powered domainfor the purpose of recreation or instruction. “eSports” refers to a formof sports competition using video games, or a multiplayer video gameplayed competitively for spectators, typically by professional garners.Digital gaming and “eSports” can be integrated into the embodiments in avariety of manners.

The term event refers to a form of play, sport, contest, or game,especially one played according to rules and decided by skill, strength,or luck. In some embodiments, an event may be football, hockey,basketball, baseball, golf, tennis, soccer, cricket, rugby, MMA, boxing,swimming, skiing, snowboarding, horse racing, car racing, boat racing,cycling, wrestling, Olympic sport, etc. Event can be integrated into theembodiments in a variety of manners.

The “total” is the combined number of runs, points or goals scored byboth teams during the game, including overtime. The “over” refers to asports bet in which the player wagers that the combined point total oftwo teams will be more than a specified total. The “under” refers tobets that the total points scored by two teams will be less than acertain figure. “Total”, “over”, and “under” can be integrated into theembodiments in a variety of manners.

A “parlay” is a single bet that links together two or more wagers; towin the bet, the player must win all the wagers in the “parlay”. If theplayer loses one wager, the player loses the entire bet. However, if hewins all the wagers in the “parlay”, the player wins a higher payoffthan if the player had placed the bets separately. A “round robin” is aseries of parlays. A “teaser” is a type of parlay in which the pointspread, or total of each individual play is adjusted. The price ofmoving the point spread (teasing) is lower payoff odds on winningwagers. “Parlay”, “round robin”, “teaser” can be integrated into theembodiments in a variety of manners.

A “prop bet” or “proposition bet” means a bet that focuses on theoutcome of events within a given game. Props are often offered onmarquee games of great interest. These include Sunday and Monday nightpro football games, various high-profile college football games, majorcollege bowl games and playoff and championship games. An example of aprop bet is “Which team will score the first touchdown?” “Prop bet” or“proposition bet” can be integrated into the embodiments in a variety ofmanners.

A “first-half bet” refers to a bet placed on the score in the first halfof the event only and only considers the first half of the game orevent. The process in which you go about placing this bet is the sameprocess that you would use to place a full game bet, but as previouslymentioned, only the first half is important to a first-half bet type ofwager. A “half-time bet” refers to a bet placed on scoring in the secondhalf of a game or event only. “First-half-bet” and “half-time-bet” canbe integrated into the embodiments in a variety of manners.

A “futures bet” or “future” refers to the odds that are posted well inadvance on the winner of major events, typical future bets are the ProFootball Championship, Collegiate Football Championship, the ProBasketball Championship, the Collegiate Basketball Championship, and thePro Baseball Championship. “Futures bet” or “future” can be integratedinto the embodiments in a variety of manners.

The “listed pitchers” is specific to a baseball bet placed only if bothof the pitchers scheduled to start a game actually start. If they don't,the bet is deemed “no action” and refunded. The “run line” in baseball,refers to a spread used instead of the money line. “Listed pitchers” and“no action” and “run line” can be integrated into the embodiments in avariety of manners.

The term “handle” refers to the total amount of bets taken. The term“hold” refers to the percentage the house wins. The term “juice” refersto the bookmaker's commission, most commonly the 11 to 10 bettors lay onstraight point spread wagers: also known as “vigorish” or “vig”. The“limit” refers to the maximum amount accepted by the house before theodds and/or point spread are changed. “Off the board” refers to a gamein which no bets are being accepted.

“Handle”, “juice”, vigorish“, “vig” and “off the board” can beintegrated into the embodiments in a variety of manners.

“Casinos” are a public room or building where gambling games are played.“Racino” is a building complex or grounds having a racetrack andgambling facilities for playing slot machines, blackjack, roulette, etc.“Casino” and “Racino” can be integrated into the embodiments in avariety of manners.

Customers are companies, organizations or individual that would deploy,for fees, and may be part of, or perform, various system elements ormethod steps in the embodiments.

Managed service user interface service is a service that can helpcustomers (1) manage third parties, (2) develop the web, (3) do dataanalytics, (4) connect thru application program interfaces and (4) trackand report on player behaviors. A managed service user interface can beintegrated into the embodiments in a variety of manners.

Managed service risk management services are services that assistscustomers with (1) very important person management, (2) businessintelligence, and (3) reporting. These managed service risk managementservices can be integrated into the embodiments in a variety of manners.

Managed service compliance service is a service that helps customersmanage (1) integrity monitoring, (2) play safety, (3) responsiblegambling and (4) customer service assistance. These managed servicecompliance services can be integrated into the embodiments in a varietyof manners.

Managed service pricing and trading service is a service that helpscustomers with (1) official data feeds, (2) data visualization and (3)land based, on property digital signage. These managed service pricingand trading services can be integrated into the embodiments in a varietyof manners.

Managed service and technology platform are services that helpscustomers with (1) web hosting, (2) IT support and (3) player accountplatform support. These managed service and technology platform servicescan be integrated into the embodiments in a variety of manners.

Managed service and marketing support services are services that helpcustomers (1) acquire and retain clients and users, (2) provide forbonusing options and (3) develop press release content generation. Thesemanaged service and marketing support services can be integrated intothe embodiments in a variety of manners.

Payment processing services are those services that help customers thatallow for (1) account auditing and (2) withdrawal processing to meetstandards for speed and accuracy. Further, these services can providefor integration of global and local payment methods. These paymentprocessing services can be integrated into the embodiments in a varietyof manners.

Engaging promotions allow customers to treat your players to free bets,odds boosts, enhanced access and flexible cashback to boost lifetimevalue. Engaging promotions can be integrated into the embodiments in avariety of manners.

“Cash out” or “pay out” or “payout” allow customers to make available,on singles bets or accumulated bets with a partial cash out where eachoperator can control payouts by managing commission and availability atall times. The “cash out” or “pay out” or “payout” can be integratedinto the embodiments in a variety of manners, including both monetaryand non-monetary payouts, such as points, prizes, promotional ordiscount codes, and the like.

“Customized betting” allow customers to have tailored personalizedbetting experiences with sophisticated tracking and analysis of players'behavior. “Customized betting” can be integrated into the embodiments ina variety of manners.

Kiosks are devices that offer interactions with customers clients andusers with a wide range of modular solutions for both retail and onlinesports gaming. Kiosks can be integrated into the embodiments in avariety of manners.

Business Applications are an integrated suite of tools for customers tomanage the everyday activities that drive sales, profit, and growth, bycreating and delivering actionable insights on performance to helpcustomers to manage the sports gaming. Business Applications can beintegrated into the embodiments in a variety of manners.

State based integration allows for a given sports gambling game to bemodified by states in the United States or other countries, based uponthe state the player is in, based upon mobile phone or other geolocationidentification means. State based integration can be integrated into theembodiments in a variety of manners.

Game Configurator allow for configuration of customer operators to havethe opportunity to apply various chosen or newly created business ruleson the game as well as to parametrize risk management. Game configuratorcan be integrated into the embodiments in a variety of manners.

“Fantasy sports connector” are software connectors between method stepsor system elements in the embodiments that can integrate fantasy sports.Fantasy sports allow a competition in which participants selectimaginary teams from among the players in a league and score pointsaccording to the actual performance of their players. For example, if aplayer in a fantasy sports is playing at a given real time sports, oddscould be changed in the real time sports for that player.

Software as a service (or SaaS) is a method of software delivery andlicensing in which software is accessed online via a subscription,rather than bought and installed on individual computers. Software as aservice can be integrated into the embodiments in a variety of manners.

Synchronization of screens means synchronizing bets and results betweendevices, such as TV and mobile, PC and wearables. Synchronization ofscreens can be integrated into the embodiments in a variety of manners.

Automatic content recognition (ACR) is an identification technology torecognize content played on a media device or present in a media file.Devices containing ACR support enable users to quickly obtain additionalinformation about the content they see without any user-based input orsearch efforts. To start the recognition, a short media clip (audio,video, or both) is selected. This clip could be selected from within amedia file or recorded by a device. Through algorithms such asfingerprinting, information from the actual perceptual content is takenand compared to a database of reference fingerprints, each referencefingerprint corresponding to a known recorded work. A database maycontain metadata about the work and associated information, includingcomplementary media. If the fingerprint of the media clip is matched,the identification software returns the corresponding metadata to theclient application. For example, during an in-play sports game a“fumble” could be recognized and at the time stamp of the event,metadata such as “fumble” could be displayed. Automatic contentrecognition (ACR) can be integrated into the embodiments in a variety ofmanners.

Joining social media means connecting an in-play sports game bet orresult to a social media connection, such as a FACEBOOK® chatinteraction. Joining social media can be integrated into the embodimentsin a variety of manners.

Augmented reality means a technology that superimposes acomputer-generated image on a user's view of the real world, thusproviding a composite view. In an example of this invention, a real timeview of the game can be seen and a “bet” which is a computer-generateddata point is placed above the player that is bet on. Augmented realitycan be integrated into the embodiments in a variety of manners.

Some embodiments of this disclosure, illustrating all its features, willnow be discussed in detail. It can be understood that the embodimentsare intended to be open ended in that an item or items used in theembodiments is not meant to be an exhaustive listing of such item oritems, or meant to be limited to only the listed item or items.

It can be noted that as used herein and in the appended claims, thesingular forms “a,” “an,” and “the” include plural references unless thecontext clearly dictates otherwise. Although any systems and methodssimilar or equivalent to those described herein can be used in thepractice or testing of embodiments, only some exemplary systems andmethods are now described.

FIG. 1 is a system for a method of displaying a notification from abetting application using AI that can impact normal betting. This systemis comprised of a live event 102, for example a sporting event such as afootball game, basketball game, baseball game, hockey game, tennismatch, golf tournament, eSports or digital game, etc. The live event 102will include some number of actions or plays, upon which a user orbettor or customer can place a bet or wager, typically through an entitycalled a sportsbook. There are numerous types of wagers the bettor canmake, including, a straight bet, a money line bet, a bet with a pointspread or line that bettor's team would need to cover, if the result ofthe game was the same as the point spread the user would not cover thespread, but instead the tie is called a push. If the user is betting onthe favorite, they are giving points to the opposing side, which is theunderdog or longshot. Betting on all favorites is referred to as chalk,this is typically applied to round robin, or other styles oftournaments. There are other types of wagers, including parlays,teasers, and prop bets, that are added games, that may allow the user tocustomize their betting by changing the odds and payouts they receive ona wager. Certain sportsbooks will allow the bettor to buy points, tomove the point spread off of the opening line, this will increase theprice of the bet, sometimes by increasing the juice, vig, or hold thatthe sportsbook takes. Another type of wager the bettor can make is anover/under, in which the user bets over or under a total for the liveevent 102, such as the score of American football or the run line inbaseball, or a series of action in the live event 102. Sportsbooks havea number of bets they can handle, a limit of wagers they can take oneither side of a bet before they will move the line or odds off of theopening line. Additionally, there are circumstance, such as an injury toan important player such as a listed pitcher, in which a sportsbook,casino or racino will take an available wager off the board. As the linemoves there becomes an opportunity for a bettor to bet on both sides atdifferent point spreads in order to middle and win both bets.Sportsbooks will often offer bets on portions of games, such as firsthalf bets and half-time bets. Additionally, the sportsbook can offerfutures bets on live events 102 in the future. Sportsbooks need to offerpayment processing services in order to cash out customers. This can bedone at kiosks at the live event 102 or at another location.

Further, embodiments may include a plurality of sensors 104 that may beused such as motion sensors, temperature sensors, humidity sensors,cameras such as an RGB-D Camera which is a digital camera capturingcolor (RGB) and depth information for every pixel in an image,microphones, a radiofrequency receiver, a thermal imager, a radardevice, a lidar device, an ultrasound device, a speaker, wearabledevices etc. Also, the plurality of sensors 104 may include trackingdevices, such as RFID tags, GPS chips or other such devices embedded onuniforms, in equipment, in the field of play, in the boundaries of thefield of play, or other markers on the field of play. Imaging devicesmay also be used as tracking devices such as player tracking thatcaptures statistical information through real-time X, Y positioning ofplayers and X, Y, Z positioning of the ball.

Further, embodiments may include a cloud 106 or communication networkwhich may be a wired and/or a wireless network. The communicationnetwork, if wireless, may be implemented using communication techniquessuch as Visible Light Communication (VLC), Worldwide Interoperabilityfor Microwave Access (WiMAX), Long Term Evolution (LTE), Wireless LocalArea Network (WLAN), Infrared (IR) communication, Public SwitchedTelephone Network (PSTN), Radio waves, and other communicationtechniques known in the art. The communication network may allowubiquitous access to shared pools of configurable system resources andhigher-level services that can be rapidly provisioned with minimalmanagement effort, such as over Internet, and relies on sharing ofresources to achieve coherence and economies of scale, like a publicutility, while third-party clouds enable organizations to focus on theircore businesses instead of expending resources on computerinfrastructure and maintenance. The cloud 106 may be communicativelycoupled to a wagering network 108 which may perform real time analysison the type of play and the result of the play. The cloud 106 may alsobe synchronized with game situational data, such as the time of thegame, the score, location on the field, weather conditions, and the likewhich may affect the choice of play utilized. For example, in someexemplary embodiments, the cloud 106 may not receive data gathered fromthe plurality of sensors 104 and may, instead, receive data from analternative data feed, such as SportsRadar®. This data may be providedsubstantially immediately following the completion of any play and thedata from this feed may be compared with a variety of team data andleague data based on a variety of elements, including down, possession,score, time, team, and so forth, as described in various exemplaryembodiments herein.

Further, embodiments may include the wagering network 108 which mayperform real time analysis on the type of play and the result of a playor action. The wagering network 108 (or cloud 106) may also besynchronized with game situational data, such as the time of the game,the score, location on the field, weather conditions, and the like whichmay affect the choice of play utilized. For example, in some exemplaryembodiments, the wagering network 108 may not receive data gathered fromthe plurality of sensors 104 and may, instead, receive data from analternative data feed, such as SportsRadar®. This data may be providedsubstantially immediately following the completion of any play and thedata from this feed may be compared with a variety of team data andleague data based on a variety of elements, including down, possession,score, time, team, and so forth, as described in various exemplaryembodiments herein. The wagering network 108 may offer a number ofsoftware as a service managed services such as, user interface service,risk management service, compliance, pricing and trading service, ITsupport of the technology platform, business applications, gameconfiguration, state based integration, fantasy sports connection,integration to allow the joining of social media, and marketing supportservices that can deliver engaging promotions to the user.

Further, embodiments may utilize a user database 110 which contains datarelevant to all users of the system, which may include, a user ID, adevice identifier, a paired device identifier, wagering history, andwallet information for each user.

Further, embodiments may include a historical play database 112, thatcontains play data for the type of sport being played in the live event102. For example, in American Football, for optimal odds calculation,the historical play data should include meta data about the historicalplays, such as time, location, weather, previous plays, opponent,physiological data, etc.

Further, embodiments may utilize an odds database 114 that contains theodds calculated by the odds calculation module, and the multipliers fordistance and path deviation, and is used for reference by the basewagering module 118 and to take bets from the user through a userinterface and calculate the payouts to the user.

Further, embodiments may utilize a betting algorithms module 116 thatcalculates odds for the next play of the live event 102 using a numberof different known algorithms, then sends the odds calculated by eachalgorithm to a cross module 118. Further, embodiments may utilize thecross module 118 which combines the odds received from the bettingalgorithms module 116 in every possible combination, for example, ifthere are 5 different odds generated from 5 different algorithms, thecross module 118 will calculate the combinations for 1 and 2, 1 and 3, 1and 4, 1 and 5, 1, 2, and 3, etc. These combinations are then stored ina cross database 120. Further, embodiments may utilize a cross database120 which contains all the combinations of odds calculated by the crossmodule 118 for the current play of the live event 102.

Further, embodiments may utilize a final odds module 122 which uses theodds stored in the cross database and the odds stored in the oddsdatabase to create the final odds for a play. The final odds module 122prompts an AI comparison module 124 to determine how all of thecalculated odds should be used in determining the final odds.

Further, embodiments may utilize the AI comparison module 124 whichprompts an odds adjustment module 128 to adjust the odds stored in theodds database 114, then compares each of the odds in the cross database120 to determine the accuracy of the odds generated by the cross module118.

Further, embodiments may utilize a machine learning module 126 whichdetermine how the historically generated final odds match the actualhistorical outcomes of plays. This data may be used to enhance theaccuracy of the final odds module 122. Further, embodiments may utilizethe odds adjustment module 128 which adjusts the odds in order tomaximize user interest while also accounting for risk of loss. The oddsadjustment module estimates profit increase due to increased userinterest and compares that value to expected loss and adjusts the oddsaccordingly in order to maximize the ratio of profit return.

Further, embodiments may utilize an odds adjustment database 130 whichstores the odds adjustment made by the odds adjustment module 128 alongwith a timestamp.

Further, embodiments may include a mobile device 132 such as a computingdevice, laptop, smartphone, tablet, computer, smart speaker, or I/Odevices. I/O devices may be present in the computing device. Inputdevices may include keyboards, mice, trackpads, trackballs, touchpads,touch mice, multi-touch touchpads and touch mice, microphones,multi-array microphones, drawing tablets, cameras, single-lens reflexcamera (SLR), digital SLR (DSLR), CMOS sensors, accelerometers, infraredoptical sensors, pressure sensors, magnetometer sensors, angular ratesensors, depth sensors, proximity sensors, ambient light sensors,gyroscopic sensors, or other sensors. Output devices may include videodisplays, graphical displays, speakers, headphones, inkjet printers,laser printers, and 3D printers. Devices may include a combination ofmultiple input or output devices, including, e.g., Microsoft KINECT,Nintendo Wii mote for the WII, Nintendo WII U GAMEPAD, or Apple IPHONE.Some devices allow gesture recognition inputs through combining some ofthe inputs and outputs. Some devices allow for facial recognition whichmay be utilized as an input for different purposes includingauthentication and other commands. Some devices provide for voicerecognition and inputs, including, e.g., Microsoft KINECT, SIRI forIPHONE by Apple, Google Now or Google Voice Search. Additional userdevices may have both input and output capabilities, including, e.g.,haptic feedback devices, touchscreen displays, or multi-touch displays.Touchscreen, multi-touch displays, touchpads, touch mice, or other touchsensing devices may use different technologies to sense touch,including, e.g., capacitive, surface capacitive, projected capacitivetouch (PCT), in-cell capacitive, resistive, infrared, waveguide,dispersive signal touch (DST), in-cell optical, surface acoustic wave(SAW), bending wave touch (BWT), or force-based sensing technologies.Some multi-touch devices may allow two or more contact points with thesurface, allowing advanced functionality including, e.g., pinch, spread,rotate, scroll, or other gestures. Some touchscreen devices, including,e.g., Microsoft PIXELSENSE or Multi-Touch Collaboration Wall, may havelarger surfaces, such as on a table-top or on a wall, and may alsointeract with other electronic devices. Some I/O devices, displaydevices or group of devices may be augmented reality devices. The I/Odevices may be controlled by an I/O controller. The I/O controller maycontrol one or more I/O devices, such as, e.g., a keyboard and apointing device, a mouse or optical pen. Furthermore, an I/O device mayalso contain storage and/or an installation medium for the computingdevice. In still other embodiments, the computing device may include USBconnections (not shown) to receive handheld USB storage devices. Infurther embodiments, an I/O device may be a bridge between the systembus and an external communication bus, e.g. a USB bus, a SCSI bus, aFireWire bus, an Ethernet bus, a Gigabit Ethernet bus, a Fiber Channelbus, or a Thunderbolt bus. In some embodiments the mobile device 132could be an optional component and may be utilized in a situation inwhich a paired wearable device is utilizing the mobile device 132 asadditional memory or computing power or as a connection to the internet.

Further, embodiments may include a wagering app 134, which is a programthat enables the user to place bets on individual plays in the liveevent 102, and display the audio and video from the live event 102,along with the available wagers on the mobile device 132. The wageringapp 134 allows the user to interact with the wagering network 108 inorder to place bets and provide payment/receive funds based on wageroutcomes.

FIG. 2 illustrates the betting algorithms module 116. The process beginswith the betting algorithms module 116 polling, at step 200, for the endof a play of the live event 102 via the plurality of sensors 104, or anyother event which would be followed by a play such as the beginning ofthe game or the end of a time-out. The betting algorithms module 116receives, at step 202, data about the current state of the live event102 from the plurality of sensors 104. For example, the live event 102features the Minnesota Vikings against the Green Bay Packers, thePackers are on offense, it is 1st and 10 and 7 minutes from half-time,and the weather is 6 mph winds. The betting algorithms module 116selects, at step 204, a first betting odds algorithm. These algorithmsare each part of the betting algorithms module 116, in some embodimentsthe algorithms may be stored in a database and retrieved. Algorithmscould be, for example, a series of steps, or even an entire separatemodule, designed to find profitable sports betting opportunities. Theyuse vast amounts of data from past sporting matches so as to identifypatterns, which can then be used to calculate the probability of certainsporting outcomes. Example algorithms include betting arbitrage, bettingbank, unit stakes, Kelly's Criterion, and Monte Carlo. The bettingalgorithms module 116 calculates, at step 206, odds for the upcomingplay of the live event 102 based on the selected algorithm. For example,algorithm A may calculate that the odds of the next play being a passare 42.4% while algorithm B calculates the odds at 46.7%. In embodimentswhere the algorithm is itself a separate module the betting algorithmsmodule 116 receives the odds instead. The betting algorithms module 116determines, at step 208, if there is another algorithm that has not yetbeen used to calculate odds for this upcoming play. If there is anotheralgorithm, the betting algorithms module 116 selects, at step 210, thenext algorithm and returns to step 206. If there are no otheralgorithms, the betting algorithms module 116 sends, at step 212, allcalculated odds to the cross module 118, then returns to step 200.

FIG. 3 illustrates the cross module 118. The process begins with thecross module 118 polling, at step 300, for a set of odds from thebetting algorithm module, each separate odds being generated by adifferent algorithm. The cross module 118 selects, at step 302, thefirst combination of odds, for example if there are five different oddsreceived from the betting algorithm module, then the first combinationwould be the first and second odds. The cross module 118 crosses, atstep 304, the combination of odds. Crossing odds is a mathematicalprocess by which odds are combined via different methods, for example,the mean of the combination of odds, the median of the combination ofodds, the mode of the combination of odds, etc. Crossing odds thenresults in multiple crossed odds outputs. For example, if we cross theodds 20%, 30%, and 50%, the results would be 33.3% for the average, but30% for the median. The cross module 118 stores, at step 306, all theresulting crossed odds in the cross database 120 along with thecombination and method of crossing. For example, the odds from algorithmA and algorithm B are crossed by crossing method A for a result of42.80%, and crossing method B for a result of 43.97%. These results arestored in the cross database. The cross module 118 determines, at step308, if there is another combination of odds that has not yet beencrossed. If there is another combination of odds, the cross module 118selects, at step 310, the next combination of odds and returns to step304. If there are no other combinations of odds, the cross module 118returns, at step 312, to step 300.

FIG. 4 illustrates the cross database 120. The cross database 120contains all the combinations of odds calculated by the cross module 118for the current play of the live event 102. Each entry contains analgorithm combination, for example, “AB” which denotes the combinationof odds generated by those algorithms, and the result of each differentcross. Cross A may be, for example, the mean value of the odds, whereascross B may be the median value. In some embodiments the cross database120 may also contain identifiers for the outcome the odds arepredicting, such as pass or run, and identifiers for which play and liveevent 102 the odds correspond to. In some embodiments the cross databasemay be purged with each new play of a live event 102.

FIG. 5 illustrates the final odds module 122. The process begins withthe final odds module 122 polling, at step 500, for new data in thecross database 120. The final odds module 122 extracts, at step 502, allentries from the cross database 120. In embodiments where the crossdatabase 120 contains crossed odds from multiple plays the final oddsmodule 122 may extract only the entries that predict the odds for theupcoming play of the live event 102. The final odds module 122 prompts,at step 504, the AI comparison module 124 for a weight for each crossedodds, meaning the odds from each unique combination of algorithms andcrossing method. The final odds module 122 receives, at step 506, a setof weights from the AI comparison module 124 corresponding to eachcrossed odds. The final odds module 122 calculates, at step 508, thefinal odds by taking a weighted average of all crossed odds. Forexample, the odds from algorithms A and B crossed by method A, 42.80%,is given a weight of 0.9 while the odds from the algorithms A and Bcrossed by method B, 43.97%, is given a weight of 0.6. The odds fromalgorithm A and B crossed by method A are multiplied by the weight 0.9,and the odds from the algorithm A and B crossed by method B aremultiplied by 0.6, the two resulting values are divided by the sum ofall weights, resulting in odds of 43.27%. The final odds module 122prompts, at step 510, the machine learning module 126 for an adjustmentfactor which is based on the historical accuracy of the final oddsmodule 122. The final odds module 122 receives, at step 512, anadjustment factor from the machine learning module 126. The final oddsmodule 122 adjusts, at step 514, the final odds based on the adjustmentfactor and then stores the final odds in the odds database 114. Forexample, the machine learning module 126 determines that the final oddspredicted passes at a 2% higher rate than the actual number of passesbased on historical data, resulting in an adjustment factor of 0.98. Theweighted average, 43.27%, is multiplied by 0.98 resulting in final oddsfor the next play being a pass of 42.40%. In some embodiments the finalodds may be stored separately from the historical odds in the oddsdatabase 114, in other embodiments the final odds will overwrite theodds already in the odds database 114. The final odds module 122returns, at step 516, to step 500. In some embodiments the final oddsmodule 122 may poll for play completion before returning to step 500.

FIG. 6 illustrates the AI comparison module 124. The process begins withthe AI comparison module 124 being, at step 600, initiated by the finalodds module 122. The AI comparison module 124 prompts, at step 602, theodds adjustment module 128 for the original odds stored in the oddsdatabase 114 which are then adjusted to optimize both profit and usersatisfaction. The AI comparison module 124 receives, at step 604, theadjusted odds from the odds adjustment module 128. The AI comparisonmodule 124 extracts, at step 606, all crossed odds from the crosseddatabase 120. In embodiments where crossed odds for more than one playare stored in the cross database 120 only crossed odds for the upcomingplay of the live event 102 will be extracted. The AI comparison module124 selects, at step 608, the first crossed odds of the extractedcrossed odds. The AI comparison module 124 compares, at step 610, theselected crossed odds with the adjusted odds and determines a weightbased on the difference. For example, the weight may be the ratio of thelesser odds divided by the greater odds or the adjusted odds divided bythe absolute value of the difference between the crossed odd and theadjusted odds. The AI comparison module 124 determines, at step 612, ifthere is another crossed odd that a weight has not yet been calculatedfor. If there is another crossed odd, the AI comparison module 124selects, at step 614, the next crossed odds and returns to step 610. Ifthere are no more crossed odds, the AI comparison module 124 sends, atstep 616, the calculated weights for each crossed odds to the final oddsmodule 122. The AI comparison module 124 ends, at step 618.

FIG. 7 illustrates the machine learning module 126. The process beginswith the machine learning module 126 being, at step 700, initiated bythe final odds module 122. The machine learning module 126 extracts, atstep 702, the final odds for the last play of the live event 102 fromthe odds database 114. The machine learning module 126 extracts, at step704, the outcome of the last play of the live event 102 from thehistoric play database 112. The machine learning module 126 increases,at step 706, the adjustment factor for the realized outcome. Forexample, if the realized outcome is a pass, the adjustment factor forodds for a pass will be increased. Adjustment factors begin at 1 and arechanged and saved with each iteration of the machine learning module126. The machine learning module 126 decreases, at step 708, theadjustment factors for the unrealized outcomes. For example, if therealized outcome is a pass, the adjustment factor for odds for a runwill be decreased. Adjustment factors begin at 1 and are changed andsaved with each iteration of the machine learning module 126. Themachine learning module 126 sends, at step 710, the relevant adjustmentfactor to the final odds module 122. For example, if the final oddsmodule 122 is only calculating the odds of a pass, then only theadjustment factor for pass odds will be sent. In some embodiments alladjustment factors may be sent and the final odds module 122 willdetermine which to use. The machine learning module 126 ends at step712.

FIG. 8 illustrates the odds adjustment module 128. The process beginswith the odds adjustment module 128 being, at step 800, initiated by theAI comparison module 124. The odds adjustment module 128 extracts, atstep 802, the odds for the upcoming play of the live event 102 from theodds database 114. The odds adjustment module 128 searches, at step 804,the historical play database 112 for plays with similar parameters tothe upcoming play of the live event 102. A play does not need to matchall of the parameters of the current state of the live event 102 inorder to be similar, for example, a similar play may be one in which thesame teams are playing but the wind speed is within 5 mph of the currentwind speed, or a play wherein the same team is on offense but adifferent team is on defense. In some embodiments the criteria for whichplays are similar is dynamic and may be determined by a machine learningalgorithm. The odds adjustment module 128 extracts, at step 804, data onall plays that are similar to the current state of the live event 102.The odds adjustment module 128 searches, at step 808, the odds database114 for the odds given to users on all of the extracted plays from thehistorical play database 112. In some embodiments different odds may begiven to different users for the same play, which would increase theaccuracy of the correlations calculated in steps 814 and 816. The oddsadjustment module 128 extracts, at step 810, all of the matching odds inthe odds database 114. The odds adjustment module 128 extracts, at step812, all data on user activity and user account balance from the userdatabase 110. User activity is the number of bets users place over agiven time period such as a week, month, year, or duration of a liveevent 102. In some embodiments user activity may also include the amountbet, or non-betting activity such as time spent on the wagering app 134.Net changes in user balance in the negative will correspond with profit.In some embodiments profit may be retrieved directly from anotherdatabase. In some embodiments incentives such as free or discountedcredits or wagers may need to be considered in order to accuratelyassess profit from net user balance changes. The odds adjustment module128 calculates, at step 814, a correlation coefficient between theextracted odds and user activity. For example, odds with better returnson plays that are similar would be expected to increase user activitybecause users would be enticed to make a bet on favorable odds. In someembodiments the correlation may be determined by linear correlation, inother embodiments the correlation may be non-linear. Correlation may becalculated using scatter diagram method, Karl Pearson's method,Spearman's rank method, least square method, or any other method knownin the art, or any combination of methods. For example, the current playof the live event 102 features the Minnesota Vikings against the GreenBay Packers, the Packers are on offense, it is 1st and 10 and 7 minutesfrom half-time, and the weather is 6 mph winds. These conditions aresimilar to other historical plays. The odds given to users for each ofthe similar historical plays is compared to the user activity level foreach play. Using Karl Pearson's method to find a linear correlationcoefficient results in a correlation coefficient of 0.85, meaning theodds given and the user activity level are highly correlated. In asecond example, the current play of the live event 102 features theMinnesota Vikings against the Green Bay Packers, the Packers are onoffense, it is 3rd and 12 and 3 minutes from the start of the game, andthe weather is light rain with no wind. These conditions are similar toother historical plays. The odds given to users for each of the similarhistorical plays is compared to the user activity level for each play.Using Karl Pearson's method to find a linear correlation coefficientresults in a correlation coefficient of 0.15, meaning the odds given andthe user activity level are not highly correlated. The odds adjustmentmodule 128 calculates, at step 816, a correlation coefficient betweenthe extracted odds and loss of user account balance, which wouldtranslate to overall profit by the system. For example, odds with betterreturns on plays that are similar would be expected to increase useraccount balance because users would be winning more money on average onmore favorable odds. In some embodiments the correlation may bedetermined by linear correlation, in other embodiments the correlationmay be non-linear. Correlation may be calculated using scatter diagrammethod, Karl Pearson's method, Spearman's rank method, least squaremethod, any other method known in the art, or any combination ofmethods. For example, the current play of the live event 102 featuresthe Minnesota Vikings against the Green Bay Packers, the Packers are onoffense, it is 1st and 10 and 7 minutes from half-time, and the weatheris 6 mph winds. These conditions are similar to other historical plays.The odds given to users for each of the similar historical plays iscompared to the profit for each play. Using Karl Pearson's method tofind a linear correlation coefficient results in a correlationcoefficient of −0.92, meaning the odds given and profit are highlycorrelated. In a second example, the current play of the live event 102features the Minnesota Vikings against the Green Bay Packers, thePackers are on offense, it is 3rd and 12 and 3 minutes from the start ofthe game, and the weather is light rain with no wind. These conditionsare similar to other historical plays. The odds given to users for eachof the similar historical plays is compared to the user activity levelfor each play. Using Karl Pearson's method to find a linear correlationcoefficient results in a correlation coefficient of −0.28, meaning theodds given and the profit are not highly correlated. The odds adjustmentmodule 128 calculates, at step 818, the ratio of user activity to profitby comparing loss of user account balance to user activity level. Forexample, each month an average of 100,000 users place an average of2,000,000 bets and on average the net profit for each month is$6,000,000. Using these numbers results in a ratio of user activity toprofit of $3 per user per month. This ratio can then be used todetermine when it can be valuable to take a loss in exchange for useractivity, for example, if the system could get 100 new users at a costof $600 the estimated time to earn back that cost would be two monthsassuming the users continue to use the system. In some embodiments thismay use the same method as the previous two steps to instead findcorrelation. The odds adjustment module 128 adjusts, at step 820, theextracted odds for the upcoming play of the live event 102. The odds areadjusted based on a calculation that considers the correlation betweenprofit and odds, the correlation between user activity and odds, theratio of user activity to profit, and a set return rate. The return rateis the rate at which the administrators of the system are willing tolose profit in the short term for more profit in the long term. Forexample, if the return rate is 10% per year then the odds adjustmentmodule will adjust the odds to be more favorable to the user such thatthe amount of profit lost will be returned with a 10% increase over thenext year due to increased user activity. If the correlation betweenodds and user activity is 0.85, and the correlation between odds andprofit is −0.92, then increasing the odds will result in more useractivity but less profit. Using the ratio of user activity to profit thesystem can then estimate an increase in odds that will return 10% profitover the next year based on the estimated immediate loss of profit andthe estimated increase in user activity due to more enticing odds. If,for example, based on historical data the odds of the next play being apass are 40%, and the odds adjustment module 128 determines that a 3%increase would result in a $500 dollar loss, but would be estimated toreturn $550 dollars due to increased user activity , the odds are thenadjusted to 43%. The odds adjustment module 128 sends, at step 822, theadjusted odds to the AI comparison Module 124. The odds adjustmentmodule 128 ends, at step 824. It may be appreciated, in some otherembodiments, that the odds adjustment module 128 may function inalternative manners rather than just adjusting odds. For example, if itis deemed desirable, based on estimated wagers or user activity, theodds adjustment module 128 could send a notification to one or moreusers who may have been determined, for example based on wageringhistory, to place a certain bet that would be desirable or provide adesirable return for an upcoming play. Alternatively, the oddsadjustment module 128 could act to lock out or prevent one or more userswho may have been determined to place a certain bet that would beundesirable or provide an undesirable return at that time. In stillother embodiments, the odds adjustment module 128 could provide anincentive to one or more users to place a certain bet that is determinedto be desirable or provide a desirable return.

FIG. 9 illustrates the odds adjustment database 130. The odds adjustmentdatabase 130 contains the original odds from the odds database 114, forexample 40%, the adjusted odds from the odds adjustment module 128, forexample 43%, and a timestamp, for example Nov. 5, 2020 4:46:19 AM.

The foregoing description and accompanying figures illustrate theprinciples, preferred embodiments and modes of operation of theinvention. However, the invention should not be construed as beinglimited to the particular embodiments discussed above. Additionalvariations of the embodiments discussed above will be appreciated bythose skilled in the art.

Therefore, the above-described embodiments should be regarded asillustrative rather than restrictive. Accordingly, it should beappreciated that variations to those embodiments can be made by thoseskilled in the art without departing from the scope of the invention asdefined by the following claims.

What is claimed is:
 1. A method of providing wagers in a wageringsystem, comprising: receiving data on a wagering system from a livesporting event upon which wagers can be placed on plays inside of thatlive event; calculating, from the data on a past play, two or moredifferent odds for at least one outcome of an upcoming play using two ormore different algorithms, wherein the two or more different odds arecalculated using at least two of a primary value betting formula, aprimary betting arbitrage formula, a betting bank formula, a unit stakesformula, a Kelly's criterion formula, and a Monte Carlo simulation;comparing context of the upcoming play to plays in a historical playdatabase; retrieving odds of historical plays in the historical playsdatabase that are similar in context to the upcoming play; andcalculating final odds for the at least one offered wager based on anadjustment factor determined by a weighted combination of the two ormore different calculated odds and the retrieved odds.
 2. The method ofproviding wagers in a wagering system of claim 1, further comprisingestimating a level of user activity of at least one offered wager, andaltering the wagering system based on the estimated level of useractivity.
 3. The method of providing wagers in a wagering system ofclaim 1, further comprising retrieving third party analytics related tothe live sporting event to determine context of the upcoming play. 4.The method of providing wagers in a wagering system of claim 2, furthercomprising changing an offered wager based on the estimated level ofuser activity.
 5. The method of providing wagers in a wagering system ofclaim 2, further comprising notifying one or more users regarding anoffered wager based on the estimated level of user activity.
 6. Themethod of providing wagers in a wagering system of claim 2, furthercomprising offering an incentive to place a wager based on the estimatedlevel of user activity.
 7. The method of providing wagers in a wageringsystem of claim 1, further comprising preventing one or more wagers fromone or more users from being placed on the wagering system.
 8. Themethod of providing wagers in a wagering system of claim 1, wherein theupcoming play is a next play in the live action game.
 9. The method ofproviding wagers in a wagering system of claim 8, wherein the upcomingplay occurs during the live action game, and the live action game has atime limit.
 10. The method of providing wagers in a wagering system ofclaim 1, wherein the calculating, in substantially real time, from thedata on the past play, of the two or more different odds for at leastone outcome of an upcoming play using two or more different algorithmsis performed automatically upon the determination that the last play hasended.
 11. The method of providing wagers in a wagering system of claim1, wherein the end of the last play is determined by at least onesensor.
 12. The method of providing wagers in a wagering system of claim1, wherein the calculating, in substantially real time, from the data onthe past play, of the two or more different odds for at least oneoutcome of an upcoming play using two or more different algorithms istriggered by occurrence of an event which would be followed by a play.13. The method of providing wagers in a wagering system of claim 1,wherein the upcoming play is after a next play.
 14. The method ofproviding wagers in a wagering system of claim 1, further comprisingcollecting wager data from at least one user related to the final odds.15. The method of providing wagers in a wagering system of claim 1,further comprising adjusting offered odds on the upcoming play based onthe collected wager data.
 16. The method of providing wagers in awagering system of claim 1, outputting the adjusted offered odds to awagering application before the upcoming play.